Pandas Profiling Report

Dataset statistics

Number of variables	19
Number of observations	45528
Missing cells	2057
Missing cells (%)	0.2%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	20.7 MiB
Average record size in memory	476.9 B

Variable types

Categorical	8
Numeric	9
Boolean	2

Alerts

`customer_id` has a high cardinality: 45528 distinct values	High cardinality
`name` has a high cardinality: 4010 distinct values	High cardinality
`no_of_children` is highly correlated with `total_family_members`	High correlation
`net_yearly_income` is highly correlated with `credit_limit`	High correlation
`total_family_members` is highly correlated with `no_of_children`	High correlation
`credit_limit` is highly correlated with `net_yearly_income`	High correlation
`prev_defaults` is highly correlated with `default_in_last_6months` and 1 other fields	High correlation
`default_in_last_6months` is highly correlated with `prev_defaults` and 1 other fields	High correlation
`credit_card_default` is highly correlated with `prev_defaults` and 1 other fields	High correlation
`no_of_children` is highly correlated with `total_family_members`	High correlation
`net_yearly_income` is highly correlated with `credit_limit`	High correlation
`total_family_members` is highly correlated with `no_of_children`	High correlation
`credit_limit` is highly correlated with `net_yearly_income`	High correlation
`credit_score` is highly correlated with `credit_card_default`	High correlation
`prev_defaults` is highly correlated with `default_in_last_6months` and 1 other fields	High correlation
`default_in_last_6months` is highly correlated with `prev_defaults` and 1 other fields	High correlation
`credit_card_default` is highly correlated with `credit_score` and 2 other fields	High correlation
`no_of_children` is highly correlated with `total_family_members`	High correlation
`net_yearly_income` is highly correlated with `credit_limit`	High correlation
`total_family_members` is highly correlated with `no_of_children`	High correlation
`credit_limit` is highly correlated with `net_yearly_income`	High correlation
`prev_defaults` is highly correlated with `default_in_last_6months` and 1 other fields	High correlation
`default_in_last_6months` is highly correlated with `prev_defaults` and 1 other fields	High correlation
`credit_card_default` is highly correlated with `prev_defaults` and 1 other fields	High correlation
`credit_card_default` is highly correlated with `default_in_last_6months` and 1 other fields	High correlation
`default_in_last_6months` is highly correlated with `credit_card_default` and 1 other fields	High correlation
`prev_defaults` is highly correlated with `credit_card_default` and 1 other fields	High correlation
`gender` is highly correlated with `occupation_type`	High correlation
`no_of_children` is highly correlated with `total_family_members`	High correlation
`net_yearly_income` is highly correlated with `credit_limit`	High correlation
`no_of_days_employed` is highly correlated with `occupation_type`	High correlation
`occupation_type` is highly correlated with `gender` and 1 other fields	High correlation
`total_family_members` is highly correlated with `no_of_children`	High correlation
`credit_limit` is highly correlated with `net_yearly_income`	High correlation
`credit_limit_used(%)` is highly correlated with `credit_card_default`	High correlation
`credit_score` is highly correlated with `prev_defaults` and 2 other fields	High correlation
`prev_defaults` is highly correlated with `credit_score` and 2 other fields	High correlation
`default_in_last_6months` is highly correlated with `credit_score` and 2 other fields	High correlation
`credit_card_default` is highly correlated with `credit_limit_used(%)` and 3 other fields	High correlation
`owns_car` has 547 (1.2%) missing values	Missing
`no_of_children` has 774 (1.7%) missing values	Missing
`no_of_days_employed` has 463 (1.0%) missing values	Missing
`net_yearly_income` is highly skewed (γ1 = 203.6835038)	Skewed
`credit_limit` is highly skewed (γ1 = 200.3871671)	Skewed
`customer_id` is uniformly distributed	Uniform
`customer_id` has unique values	Unique
`no_of_children` has 31241 (68.6%) zeros	Zeros

Reproduction

Analysis started	2021-12-14 18:08:23.667019
Analysis finished	2021-12-14 18:08:52.691728
Duration	29.02 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

customer_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	45528
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	2.9 MiB

CST_115179	1
CST_115723	1
CST_156690	1
CST_136647	1
CST_120077	1
Other values (45523)	45523

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	45528 ?
Unique (%)	100.0%

Sample

1st row	CST_115179
2nd row	CST_121920
3rd row	CST_109330
4th row	CST_128288
5th row	CST_151355

Common Values

Value	Count	Frequency (%)
CST_115179	1	< 0.1%
CST_115723	1	< 0.1%
CST_156690	1	< 0.1%
CST_136647	1	< 0.1%
CST_120077	1	< 0.1%
CST_123382	1	< 0.1%
CST_134690	1	< 0.1%
CST_134080	1	< 0.1%
CST_122892	1	< 0.1%
CST_127652	1	< 0.1%
Other values (45518)	45518	> 99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
cst_115179	1	< 0.1%
cst_145999	1	< 0.1%
cst_151332	1	< 0.1%
cst_153773	1	< 0.1%
cst_109330	1	< 0.1%
cst_128288	1	< 0.1%
cst_151355	1	< 0.1%
cst_123268	1	< 0.1%
cst_127502	1	< 0.1%
cst_151722	1	< 0.1%
Other values (45518)	45518	> 99.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

name
Categorical

HIGH CARDINALITY

Distinct	4010
Distinct (%)	8.8%
Missing	0
Missing (%)	0.0%
Memory size	2.9 MiB

Jonathan	207
Jonathan Stempel	192
David	170
Stempel	165
Jessica	162
Other values (4005)	44632

Length

Max length	27
Median length	8
Mean length	8.746397821
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1226 ?
Unique (%)	2.7%

Sample

1st row	ita Bose
2nd row	Alper Jonathan
3rd row	Umesh Desai
4th row	Rie
5th row	McCool

Common Values

Value	Count	Frequency (%)
Jonathan	207	0.5%
Jonathan Stempel	192	0.4%
David	170	0.4%
Stempel	165	0.4%
Jessica	162	0.4%
Sarah	147	0.3%
Lucia	142	0.3%
Nick	140	0.3%
Lucia Mutikani	137	0.3%
Jones	133	0.3%
Other values (4000)	43933	96.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
jonathan	591	0.9%
david	534	0.8%
sarah	386	0.6%
john	381	0.6%
jessica	380	0.6%
stempel	358	0.6%
jones	350	0.6%
paul	339	0.5%
kim	335	0.5%
nick	334	0.5%
Other values (2369)	59643	93.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ_≥0)

Distinct	33
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	38.99341065

Minimum	23
Maximum	55
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	355.8 KiB

Quantile statistics

Minimum	23
5-th percentile	24
Q1	31
median	39
Q3	47
95-th percentile	54
Maximum	55
Range	32
Interquartile range (IQR)	16

Descriptive statistics

Standard deviation	9.543990289
Coefficient of variation (CV)	0.2447590537
Kurtosis	-1.203387195
Mean	38.99341065
Median Absolute Deviation (MAD)	8
Skewness	0.003974890882
Sum	1775292
Variance	91.08775064
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=33)

Value	Count	Frequency (%)
40	1455	3.2%
55	1448	3.2%
36	1420	3.1%
23	1411	3.1%
35	1410	3.1%
37	1409	3.1%
49	1408	3.1%
26	1399	3.1%
48	1396	3.1%
27	1395	3.1%
Other values (23)	31377	68.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
23	1411	3.1%
24	1369	3.0%
25	1373	3.0%
26	1399	3.1%
27	1395	3.1%
28	1368	3.0%
29	1395	3.1%
30	1384	3.0%
31	1373	3.0%
32	1380	3.0%

Value	Count	Frequency (%)
55	1448	3.2%
54	1383	3.0%
53	1385	3.0%
52	1345	3.0%
51	1371	3.0%
50	1377	3.0%
49	1408	3.1%
48	1396	3.1%
47	1314	2.9%
46	1364	3.0%

gender
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	2.5 MiB

F	29957
M	15570
XNA	1

Length

Max length	3
Median length	1
Mean length	1.000043929
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	F
2nd row	M
3rd row	M
4th row	F
5th row	M

Common Values

Value	Count	Frequency (%)
F	29957	65.8%
M	15570	34.2%
XNA	1	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
f	29957	65.8%
m	15570	34.2%
xna	1	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

owns_car
Boolean

MISSING

Distinct	2
Distinct (%)	< 0.1%
Missing	547
Missing (%)	1.2%
Memory size	89.0 KiB

False	29743
True	15238
(Missing)	547

Common Values
Chart

Value	Count	Frequency (%)
False	29743	65.3%
True	15238	33.5%
(Missing)	547	1.2%

owns_house
Boolean

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	44.6 KiB

True	31642
False	13886

Common Values
Chart

Value	Count	Frequency (%)
True	31642	69.5%
False	13886	30.5%

no_of_children
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct	10
Distinct (%)	< 0.1%
Missing	774
Missing (%)	1.7%
Infinite	0
Infinite (%)	0.0%
Mean	0.420655137

Minimum	0
Maximum	9
Zeros	31241
Zeros (%)	68.6%
Negative	0
Negative (%)	0.0%
Memory size	355.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	2
Maximum	9
Range	9
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	0.7240971792
Coefficient of variation (CV)	1.721355846
Kurtosis	3.815248833
Mean	0.420655137
Median Absolute Deviation (MAD)	0
Skewness	1.827606343
Sum	18826
Variance	0.5243167249
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
0	31241	68.6%
1	8985	19.7%
2	3862	8.5%
3	584	1.3%
4	60	0.1%
5	13	< 0.1%
6	6	< 0.1%
7	1	< 0.1%
8	1	< 0.1%
9	1	< 0.1%
(Missing)	774	1.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	31241	68.6%
1	8985	19.7%
2	3862	8.5%
3	584	1.3%
4	60	0.1%
5	13	< 0.1%
6	6	< 0.1%
7	1	< 0.1%
8	1	< 0.1%
9	1	< 0.1%

Value	Count	Frequency (%)
9	1	< 0.1%
8	1	< 0.1%
7	1	< 0.1%
6	6	< 0.1%
5	13	< 0.1%
4	60	0.1%
3	584	1.3%
2	3862	8.5%
1	8985	19.7%
0	31241	68.6%

net_yearly_income
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct	45502
Distinct (%)	99.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	200655.6222

Minimum	27170.61
Maximum	140759012.7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	355.8 KiB

Quantile statistics

Minimum	27170.61
5-th percentile	77893.5085
Q1	126345.835
median	171714.91
Q3	240603.76
95-th percentile	392349.715
Maximum	140759012.7
Range	140731842.1
Interquartile range (IQR)	114257.925

Descriptive statistics

Standard deviation	669074.0345
Coefficient of variation (CV)	3.334439509
Kurtosis	42784.72019
Mean	200655.6222
Median Absolute Deviation (MAD)	54040.965
Skewness	203.6835038
Sum	9135449170
Variance	4.476600636 × 10¹¹
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
174102.44	2	< 0.1%
228141.82	2	< 0.1%
130685.66	2	< 0.1%
136189.92	2	< 0.1%
128915	2	< 0.1%
249134.47	2	< 0.1%
274014.39	2	< 0.1%
110317.4	2	< 0.1%
151482.74	2	< 0.1%
100156.16	2	< 0.1%
Other values (45492)	45508	> 99.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
27170.61	1	< 0.1%
28532.17	1	< 0.1%
29191.13	1	< 0.1%
29453.34	1	< 0.1%
30176.76	1	< 0.1%
30270.45	1	< 0.1%
30393.01	1	< 0.1%
31261.55	1	< 0.1%
31489.64	1	< 0.1%
31750.01	1	< 0.1%

Value	Count	Frequency (%)
140759012.7	1	< 0.1%
4433825.02	1	< 0.1%
4193101.77	1	< 0.1%
2784729.58	1	< 0.1%
2451896.42	1	< 0.1%
2413494.72	1	< 0.1%
2408259.11	1	< 0.1%
2217660.82	1	< 0.1%
2091355.91	1	< 0.1%
1947528.14	1	< 0.1%

no_of_days_employed
Real number (ℝ_≥0)

HIGH CORRELATION
MISSING

Distinct	7874
Distinct (%)	17.5%
Missing	463
Missing (%)	1.0%
Infinite	0
Infinite (%)	0.0%
Mean	67609.28929

Minimum	2
Maximum	365252
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	355.8 KiB

Quantile statistics

Minimum	2
5-th percentile	227
Q1	936
median	2224
Q3	5817
95-th percentile	365249
Maximum	365252
Range	365250
Interquartile range (IQR)	4881

Descriptive statistics

Standard deviation	139323.5244
Coefficient of variation (CV)	2.060715708
Kurtosis	0.7827392915
Mean	67609.28929
Median Absolute Deviation (MAD)	1608
Skewness	1.667674633
Sum	3046812622
Variance	1.941104446 × 10¹⁰
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
365246	684	1.5%
365244	669	1.5%
365240	641	1.4%
365245	631	1.4%
365241	628	1.4%
365247	625	1.4%
365250	609	1.3%
365251	607	1.3%
365243	607	1.3%
365252	602	1.3%
Other values (7864)	38762	85.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2	1	< 0.1%
3	1	< 0.1%
6	1	< 0.1%
12	1	< 0.1%
13	1	< 0.1%
17	2	< 0.1%
21	1	< 0.1%
23	1	< 0.1%
24	1	< 0.1%
25	1	< 0.1%

Value	Count	Frequency (%)
365252	602	1.3%
365251	607	1.3%
365250	609	1.3%
365249	601	1.3%
365248	594	1.3%
365247	625	1.4%
365246	684	1.5%
365245	631	1.4%
365244	669	1.5%
365243	607	1.3%

occupation_type
Categorical

HIGH CORRELATION

Distinct	19
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	2.9 MiB

Unknown	14299
Laborers	8134
Sales staff	4725
Core staff	4062
Managers	3168
Other values (14)	11140

Length

Max length	21
Median length	8
Mean length	9.444605517
Min length	7

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Unknown
2nd row	Laborers
3rd row	Laborers
4th row	Core staff
5th row	Core staff

Common Values

Value	Count	Frequency (%)
Unknown	14299	31.4%
Laborers	8134	17.9%
Sales staff	4725	10.4%
Core staff	4062	8.9%
Managers	3168	7.0%
Drivers	2747	6.0%
High skill tech staff	1682	3.7%
Accountants	1474	3.2%
Medicine staff	1275	2.8%
Security staff	1025	2.3%
Other values (9)	2937	6.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
staff	15070	23.3%
unknown	14299	22.1%
laborers	8470	13.1%
sales	4725	7.3%
core	4062	6.3%
managers	3168	4.9%
drivers	2747	4.2%
high	1682	2.6%
skill	1682	2.6%
tech	1682	2.6%
Other values (14)	7199	11.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

total_family_members
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	10
Distinct (%)	< 0.1%
Missing	83
Missing (%)	0.2%
Infinite	0
Infinite (%)	0.0%
Mean	2.158081197

Minimum	1
Maximum	10
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	355.8 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	2
Q3	3
95-th percentile	4
Maximum	10
Range	9
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	0.9115724693
Coefficient of variation (CV)	0.4223995235
Kurtosis	1.335041586
Mean	2.158081197
Median Absolute Deviation (MAD)	0
Skewness	0.9248241035
Sum	98074
Variance	0.8309643667
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
2	23455	51.5%
1	9913	21.8%
3	7812	17.2%
4	3623	8.0%
5	564	1.2%
6	57	0.1%
7	12	< 0.1%
8	6	< 0.1%
10	2	< 0.1%
9	1	< 0.1%
(Missing)	83	0.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	9913	21.8%
2	23455	51.5%
3	7812	17.2%
4	3623	8.0%
5	564	1.2%
6	57	0.1%
7	12	< 0.1%
8	6	< 0.1%
9	1	< 0.1%
10	2	< 0.1%

Value	Count	Frequency (%)
10	2	< 0.1%
9	1	< 0.1%
8	6	< 0.1%
7	12	< 0.1%
6	57	0.1%
5	564	1.2%
4	3623	8.0%
3	7812	17.2%
2	23455	51.5%
1	9913	21.8%

migrant_worker
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	87
Missing (%)	0.2%
Memory size	2.6 MiB

0.0	37302
1.0	8139

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1.0
2nd row	0.0
3rd row	0.0
4th row	0.0
5th row	0.0

Common Values

Value	Count	Frequency (%)
0.0	37302	81.9%
1.0	8139	17.9%
(Missing)	87	0.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0.0	37302	82.1%
1.0	8139	17.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

yearly_debt_payments
Real number (ℝ_≥0)

Distinct	45251
Distinct (%)	99.6%
Missing	95
Missing (%)	0.2%
Infinite	0
Infinite (%)	0.0%
Mean	31796.96531

Minimum	2237.47
Maximum	328112.86
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	355.8 KiB

Quantile statistics

Minimum	2237.47
5-th percentile	10354.064
Q1	19231.14
median	29081.65
Q3	40561.15
95-th percentile	62796.392
Maximum	328112.86
Range	325875.39
Interquartile range (IQR)	21330.01

Descriptive statistics

Standard deviation	17269.72723
Coefficient of variation (CV)	0.5431250141
Kurtosis	9.772470795
Mean	31796.96531
Median Absolute Deviation (MAD)	10514.01
Skewness	1.721201453
Sum	1444631525
Variance	298243478.7
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
23213.37	2	< 0.1%
14243.92	2	< 0.1%
42710.33	2	< 0.1%
31606.52	2	< 0.1%
22534.21	2	< 0.1%
30683.36	2	< 0.1%
39822.63	2	< 0.1%
25862.02	2	< 0.1%
38694.1	2	< 0.1%
17794.72	2	< 0.1%
Other values (45241)	45413	99.7%
(Missing)	95	0.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2237.47	1	< 0.1%
2752.11	1	< 0.1%
2881.98	1	< 0.1%
3139.06	1	< 0.1%
3230.67	1	< 0.1%
3256.33	1	< 0.1%
3324.48	1	< 0.1%
3328.38	1	< 0.1%
3369.88	1	< 0.1%
3506.05	1	< 0.1%

Value	Count	Frequency (%)
328112.86	1	< 0.1%
279269.56	1	< 0.1%
276512.94	1	< 0.1%
274939.59	1	< 0.1%
255108.89	1	< 0.1%
231222.57	1	< 0.1%
221725.74	1	< 0.1%
219235.54	1	< 0.1%
213002.3	1	< 0.1%
199119.18	1	< 0.1%

credit_limit
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct	45371
Distinct (%)	99.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	43548.41603

Minimum	4003.14
Maximum	31129970.49
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	355.8 KiB

Quantile statistics

Minimum	4003.14
5-th percentile	13356.867
Q1	23973.805
median	35688.045
Q3	53435.7625
95-th percentile	94836.2125
Maximum	31129970.49
Range	31125967.35
Interquartile range (IQR)	29461.9575

Descriptive statistics

Standard deviation	148784.6869
Coefficient of variation (CV)	3.416534984
Kurtosis	41860.88255
Mean	43548.41603
Median Absolute Deviation (MAD)	13778.625
Skewness	200.3871671
Sum	1982672285
Variance	2.213688305 × 10¹⁰
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
33429.22	2	< 0.1%
27557.56	2	< 0.1%
42343.39	2	< 0.1%
47321.45	2	< 0.1%
20353.39	2	< 0.1%
24673.1	2	< 0.1%
17772.02	2	< 0.1%
37674.2	2	< 0.1%
21793.67	2	< 0.1%
44933.96	2	< 0.1%
Other values (45361)	45508	> 99.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
4003.14	1	< 0.1%
4030.68	1	< 0.1%
4042.77	1	< 0.1%
4210.96	1	< 0.1%
4219.32	1	< 0.1%
4328.61	1	< 0.1%
4328.99	1	< 0.1%
4356.15	1	< 0.1%
4483.55	1	< 0.1%
4601.3	1	< 0.1%

Value	Count	Frequency (%)
31129970.49	1	< 0.1%
1015611.88	1	< 0.1%
841596.19	1	< 0.1%
817713.83	1	< 0.1%
648100.99	1	< 0.1%
621953.64	1	< 0.1%
610931.7	1	< 0.1%
580567.35	1	< 0.1%
548115.45	1	< 0.1%
477006.23	1	< 0.1%

credit_limit_used(%)
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	100
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	52.23502021

Minimum	0
Maximum	99
Zeros	430
Zeros (%)	0.9%
Negative	0
Negative (%)	0.0%
Memory size	355.8 KiB

Quantile statistics

Minimum	0
5-th percentile	5
Q1	27
median	54
Q3	78
95-th percentile	95
Maximum	99
Range	99
Interquartile range (IQR)	51

Descriptive statistics

Standard deviation	29.3769096
Coefficient of variation (CV)	0.562398741
Kurtosis	-1.243307626
Mean	52.23502021
Median Absolute Deviation (MAD)	26
Skewness	-0.1274493369
Sum	2378156
Variance	863.0028178
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
90	584	1.3%
81	571	1.3%
87	569	1.2%
89	568	1.2%
84	566	1.2%
78	564	1.2%
72	558	1.2%
80	557	1.2%
99	556	1.2%
85	554	1.2%
Other values (90)	39881	87.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	430	0.9%
1	389	0.9%
2	433	1.0%
3	431	0.9%
4	411	0.9%
5	439	1.0%
6	437	1.0%
7	423	0.9%
8	429	0.9%
9	425	0.9%

Value	Count	Frequency (%)
99	556	1.2%
98	513	1.1%
97	540	1.2%
96	524	1.2%
95	522	1.1%
94	472	1.0%
93	507	1.1%
92	549	1.2%
91	513	1.1%
90	584	1.3%

credit_score
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	450
Distinct (%)	1.0%
Missing	8
Missing (%)	< 0.1%
Infinite	0
Infinite (%)	0.0%
Mean	782.7912566

Minimum	500
Maximum	949
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	355.8 KiB

Quantile statistics

Minimum	500
5-th percentile	621.95
Q1	704
median	786
Q3	867
95-th percentile	933
Maximum	949
Range	449
Interquartile range (IQR)	163

Descriptive statistics

Standard deviation	100.6197458
Coefficient of variation (CV)	0.1285396904
Kurtosis	-0.5473691428
Mean	782.7912566
Median Absolute Deviation (MAD)	81.5
Skewness	-0.3025168255
Sum	35632658
Variance	10124.33323
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
670	186	0.4%
660	183	0.4%
684	176	0.4%
682	175	0.4%
651	175	0.4%
662	174	0.4%
681	172	0.4%
691	171	0.4%
692	169	0.4%
659	168	0.4%
Other values (440)	43771	96.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
500	11	< 0.1%
501	19	< 0.1%
502	21	< 0.1%
503	18	< 0.1%
504	11	< 0.1%
505	17	< 0.1%
506	20	< 0.1%
507	14	< 0.1%
508	20	< 0.1%
509	19	< 0.1%

Value	Count	Frequency (%)
949	146	0.3%
948	151	0.3%
947	131	0.3%
946	138	0.3%
945	123	0.3%
944	126	0.3%
943	146	0.3%
942	148	0.3%
941	151	0.3%
940	139	0.3%

prev_defaults
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	2.5 MiB

0	43060
1	2172
2	296

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	43060	94.6%
1	2172	4.8%
2	296	0.7%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	43060	94.6%
1	2172	4.8%
2	296	0.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

default_in_last_6months
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	2.5 MiB

0	43227
1	2301

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	43227	94.9%
1	2301	5.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	43227	94.9%
1	2301	5.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

credit_card_default
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	2.5 MiB

0	41831
1	3697

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	41831	91.9%
1	3697	8.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	41831	91.9%
1	3697	8.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	customer_id	name	age	gender	owns_car	owns_house	no_of_children	net_yearly_income	no_of_days_employed	occupation_type	total_family_members	migrant_worker	yearly_debt_payments	credit_limit	credit_limit_used(%)	credit_score	prev_defaults	default_in_last_6months	credit_card_default
0	CST_115179	ita Bose	46	F	N	Y	0.00	107934.04	612.00	Unknown	1.00	1.00	33070.28	18690.93	73	544.00	2	1	1
1	CST_121920	Alper Jonathan	29	M	N	Y	0.00	109862.62	2771.00	Laborers	2.00	0.00	15329.53	37745.19	52	857.00	0	0	0
2	CST_109330	Umesh Desai	37	M	N	Y	0.00	230153.17	204.00	Laborers	2.00	0.00	48416.60	41598.36	43	650.00	0	0	0
3	CST_128288	Rie	39	F	N	Y	0.00	122325.82	11941.00	Core staff	2.00	0.00	22574.36	32627.76	20	754.00	0	0	0
4	CST_151355	McCool	46	M	Y	Y	0.00	387286.00	1459.00	Core staff	1.00	0.00	38282.95	52950.64	75	927.00	0	0	0
5	CST_123268	Sarah Marsh	46	F	Y	N	0.00	252765.91	2898.00	Accountants	2.00	1.00	37046.86	40245.64	19	937.00	0	0	0
6	CST_127502	Mason	38	M	N	Y	1.00	262389.20	5541.00	High skill tech staff	3.00	0.00	50839.39	41311.08	42	733.00	0	0	0
7	CST_151722	Saba	46	F	Y	Y	1.00	241211.39	1448.00	Core staff	3.00	0.00	30008.46	32209.22	91	906.00	0	0	0
8	CST_133768	Ashutosh	40	F	NaN	Y	0.00	210091.43	11551.00	Laborers	2.00	0.00	21521.89	65037.74	14	783.00	0	0	0
9	CST_111670	David Milliken	39	F	Y	Y	2.00	207109.13	2791.00	High skill tech staff	4.00	0.00	9509.10	28425.52	14	666.00	0	0	0

Last rows

	customer_id	name	age	gender	owns_car	owns_house	no_of_children	net_yearly_income	no_of_days_employed	occupation_type	total_family_members	migrant_worker	yearly_debt_payments	credit_limit	credit_limit_used(%)	credit_score	credit_card_default
45518	CST_112481	David Bailey	54	F	N	Y	2.00	252941.68	2266.00	Laborers	3.00	0.00	37513.38	45775.43	71	677.00	1
45519	CST_144544	Emily	41	M	Y	Y	2.00	293494.24	794.00	Core staff	4.00	0.00	40402.37	83883.37	97	665.00	0
45520	CST_137966	Vladimir	47	M	Y	Y	0.00	291628.76	1677.00	Drivers	1.00	0.00	15627.73	42980.82	85	915.00	0
45521	CST_161068	Dye	48	F	N	Y	0.00	89435.47	365249.00	Unknown	2.00	0.00	31233.88	21850.77	36	879.00	0
45522	CST_120545	Tim Hepher	54	F	N	N	1.00	138001.12	161.00	Unknown	3.00	0.00	16609.04	18565.63	71	893.00	0
45523	CST_130421	Doris	55	F	N	N	2.00	96207.57	117.00	Unknown	4.00	0.00	11229.54	29663.83	82	907.00	0
45524	CST_136670	Luciana	31	F	N	Y	0.00	383476.74	966.00	Accountants	2.00	1.00	43369.91	139947.16	32	679.00	0
45525	CST_145435	Jessica	27	F	N	Y	0.00	260052.18	1420.00	Core staff	2.00	0.00	22707.51	83961.83	46	727.00	0
45526	CST_130913	Tessa	32	M	Y	N	0.00	157363.04	2457.00	Laborers	2.00	0.00	20150.10	25538.72	92	805.00	0
45527	CST_160078	Gopinath	38	M	N	Y	1.00	316896.28	1210.00	Unknown	3.00	0.00	34603.78	36630.76	26	682.00	0

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions